
"""simple docstring"""



from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    for param, grad_param in zip(model_a.parameters()       ,   model_b.parameters()   ):
        if not param.requires_grad:
            continue
        if not did_step:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad       ,   grad_param.grad   ) is False
            ), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
        else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad       ,   grad_param.grad   ) is True
            ), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A=True   )     ->	Any:





    """simple docstring"""

    model.train()
    _UpperCAmelCase        = model(UpperCamelCase_   )
    _UpperCAmelCase        = F.mse_loss(UpperCamelCase_       ,   target.to(output.device   )   )
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(UpperCamelCase_   )


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	List[str]:





    """simple docstring"""

    set_seed(4_2   )
    _UpperCAmelCase        = RegressionModel()
    _UpperCAmelCase        = deepcopy(UpperCamelCase_   )
    _UpperCAmelCase        = RegressionDataset(length=8_0   )
    _UpperCAmelCase        = DataLoader(UpperCamelCase_       ,   batch_size=1_6   )
    model.to(accelerator.device   )
    if sched:
        _UpperCAmelCase        = AdamW(params=model.parameters()       ,   lr=1e-3   )
        _UpperCAmelCase        = AdamW(params=ddp_model.parameters()       ,   lr=1e-3   )
        _UpperCAmelCase        = LambdaLR(UpperCamelCase_       ,   lr_lambda=lambda _A   : epoch**0.65   )
        _UpperCAmelCase        = LambdaLR(UpperCamelCase_       ,   lr_lambda=lambda _A   : epoch**0.65   )
    # Make a copy of `model`
    if sched:
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = accelerator.prepare(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
    else:
        _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.prepare(UpperCamelCase_       ,   UpperCamelCase_   )
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = get_training_setup(UpperCamelCase_   )
    # Use a single batch
    _UpperCAmelCase     ,_UpperCAmelCase        = next(iter(UpperCamelCase_   )   ).values()
    for iteration in range(3   ):
        # Gather the distributed inputs and targs for the base model
        _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.gather((ddp_input, ddp_target)   )
        _UpperCAmelCase     ,_UpperCAmelCase        = input.to(accelerator.device   ), target.to(accelerator.device   )
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(UpperCamelCase_   ):
                step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
        else:
            # Sync grads
            step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
        for param, ddp_param in zip(model.parameters()       ,   ddp_model.parameters()   ):
            if not param.requires_grad:
                continue
            assert torch.allclose(
                param.grad       ,   ddp_param.grad   ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_3_3_7 + iteration   )
        _UpperCAmelCase        = ddp_input[torch.randperm(len(UpperCamelCase_   )   )]


def 			_UpperCamelCase						(    _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = get_training_setup(UpperCamelCase_   )
    # Use a single batch
    _UpperCAmelCase     ,_UpperCAmelCase        = next(iter(UpperCamelCase_   )   ).values()
    for iteration in range(3   ):
        # Gather the distributed inputs and targs for the base model
        _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.gather((ddp_input, ddp_target)   )
        _UpperCAmelCase     ,_UpperCAmelCase        = input.to(accelerator.device   ), target.to(accelerator.device   )
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(UpperCamelCase_   ):
                step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
        else:
            # Sync grads
            step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()       ,   ddp_model.parameters()   ):
            if not param.requires_grad:
                continue
            if iteration % 2 == 0:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad       ,   ddp_param.grad   ) is False
                ), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
            else:
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad       ,   ddp_param.grad   ) is True
                ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

                    # Shuffle ddp_input on each iteration
        torch.manual_seed(1_3_3_7 + iteration   )
        _UpperCAmelCase        = ddp_input[torch.randperm(len(UpperCamelCase_   )   )]


def 			_UpperCamelCase						(    _A=False       ,   _A=False   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = Accelerator(
        split_batches=UpperCamelCase_       ,   dispatch_batches=UpperCamelCase_       ,   gradient_accumulation_steps=2   )
    # Test that context manager behaves properly
    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = get_training_setup(UpperCamelCase_   )
    for iteration, batch in enumerate(UpperCamelCase_   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = batch.values()
        # Gather the distributed inputs and targs for the base model
        _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.gather((ddp_input, ddp_target)   )
        _UpperCAmelCase     ,_UpperCAmelCase        = input.to(accelerator.device   ), target.to(accelerator.device   )
        # Perform our initial ground truth step in non "DDP"
        step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
        # Do "gradient accumulation" (noop)
        with accelerator.accumulate(UpperCamelCase_   ):
            step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()       ,   ddp_model.parameters()   ):
            if not param.requires_grad:
                continue
            if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase_   ) - 1):
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad       ,   ddp_param.grad   ) is True
                ), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
            else:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad       ,   ddp_param.grad   ) is False
                ), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

                    # Shuffle ddp_input on each iteration
        torch.manual_seed(1_3_3_7 + iteration   )
        _UpperCAmelCase        = ddp_input[torch.randperm(len(UpperCamelCase_   )   )]
    GradientState._reset_state()


def 			_UpperCamelCase						(    _A=False       ,   _A=False   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = Accelerator(
        split_batches=UpperCamelCase_       ,   dispatch_batches=UpperCamelCase_       ,   gradient_accumulation_steps=2   )
    # Test that context manager behaves properly
    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = get_training_setup(UpperCamelCase_       ,   UpperCamelCase_   )
    for iteration, batch in enumerate(UpperCamelCase_   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = batch.values()
        # Gather the distributed inputs and targs for the base model
        _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.gather((ddp_input, ddp_target)   )
        _UpperCAmelCase     ,_UpperCAmelCase        = input.to(accelerator.device   ), target.to(accelerator.device   )
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase_   )):
            if split_batches:
                sched.step()
            else:
                for _ in range(accelerator.num_processes   ):
                    sched.step()
        opt.zero_grad()
        # Perform gradient accumulation under wrapper
        with accelerator.accumulate(UpperCamelCase_   ):
            step_model(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
            ddp_opt.step()
            ddp_sched.step()
            ddp_opt.zero_grad()

        # Learning rates should be the same
        assert (
            opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
        ), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
        _UpperCAmelCase        = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase_   ))
        if accelerator.num_processes > 1:
            check_model_parameters(UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_       ,   UpperCamelCase_   )
        # Shuffle ddp_input on each iteration
        torch.manual_seed(1_3_3_7 + iteration   )
    GradientState._reset_state()


def 			_UpperCamelCase						(    )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = Accelerator()

    _UpperCAmelCase        = RegressionDataset(length=8_0   )
    _UpperCAmelCase        = DataLoader(UpperCamelCase_       ,   batch_size=1_6   )
    _UpperCAmelCase        = RegressionDataset(length=9_6   )
    _UpperCAmelCase        = DataLoader(UpperCamelCase_       ,   batch_size=1_6   )
    _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.prepare(UpperCamelCase_       ,   UpperCamelCase_   )
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(UpperCamelCase_   ):
        assert id(accelerator.gradient_state.active_dataloader   ) == id(UpperCamelCase_   )
        if iteration < len(UpperCamelCase_   ) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(UpperCamelCase_   ):
                    assert id(accelerator.gradient_state.active_dataloader   ) == id(UpperCamelCase_   )
                    if batch_num < len(UpperCamelCase_   ) - 1:
                        assert not accelerator.gradient_state.end_of_dataloader
                    else:
                        assert accelerator.gradient_state.end_of_dataloader
        else:
            assert accelerator.gradient_state.end_of_dataloader
    assert accelerator.gradient_state.active_dataloader is None


def 			_UpperCamelCase						(    )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = Accelerator()
    _UpperCAmelCase        = accelerator.state
    if state.local_process_index == 0:
        print("""**Test `accumulate` gradient accumulation with dataloader break**"""   )
    test_dataloader_break()
    if state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print("""**Test NOOP `no_sync` context manager**"""   )
        test_noop_sync(UpperCamelCase_   )
    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
        if state.local_process_index == 0:
            print("""**Test Distributed `no_sync` context manager**"""   )
        test_distributed_sync(UpperCamelCase_   )
    if state.distributed_type == DistributedType.MULTI_GPU:
        for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
                if state.local_process_index == 0:
                    print(
                        """**Test `accumulate` gradient accumulation, """       ,   F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""       ,   )
                test_gradient_accumulation(UpperCamelCase_       ,   UpperCamelCase_   )

                # Currently will break on torch 2.0 +, need to investigate why
    if is_torch_version("""<"""       ,   """2.0"""   ) or state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print(
                """**Test `accumulate` gradient accumulation with optimizer and scheduler, """       ,   """`split_batches=False`, `dispatch_batches=False`**"""       ,   )
        test_gradient_accumulation_with_opt_and_scheduler()
        if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                for dispatch_batches in [True, False]:
                    if not split_batch and not dispatch_batches:
                        continue
                    if state.local_process_index == 0:
                        print(
                            """**Test `accumulate` gradient accumulation with optimizer and scheduler, """       ,   F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""       ,   )
                    test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase_       ,   UpperCamelCase_   )


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    main()


if __name__ == "__main__":
 main()
from __future__ import annotations
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      text, pattern
    UpperCamelCase  ,     UpperCamelCase			  =      len(lowerCamelCase_				), len(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if char == self.pattern[i]:
      return i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if self.pattern[i] != self.text[current_pos + i]:
      return current_pos + i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for i in range(self.textLen - self.patLen + 1				):
     UpperCamelCase			  =      self.mismatch_in_text(lowerCamelCase_				)
     if mismatch_index == -1:
      positions.append(lowerCamelCase_				)
     else:
      UpperCamelCase			  =      self.match_in_pattern(self.text[mismatch_index]				)
      UpperCamelCase			  =      (
          mismatch_index - match_index
      )  # shifting index lgtm [py/multiple-definition]
    return positions





_SCREAMING_SNAKE_CASE 		=      """ABAABA"""
_SCREAMING_SNAKE_CASE 		=      """AB"""
_SCREAMING_SNAKE_CASE 		=      BoyerMooreSearch(text, pattern)
_SCREAMING_SNAKE_CASE 		=      bms.bad_character_heuristic()

if len(positions) == 0:
     print("""No match found""")
else:
     print("""Pattern found in following positions: """)
     print(positions)
